Radiotherapy is in a process of transformation from image-guided radiotherapy to biologically guided radiotherapy [ quantitative methods in diffusion-weighted imaging- (DW-) MRI providing ADC (apparent diffusion coefficient) maps that allow determining early tumour response [ in vivo measurement of hypoxia [ inverse-planning optimisation algorithm that includes biological criteria [
In this paper a case study is presented using datasets from 18F-FDG (fludeoxyglucose labelled with 18F) PET/CT, DW-MRI/ADC maps, and dynamic contrast-enhanced- (DCE-) MRI for characterizing tumour behaviour and for using the multimodality parameters as predictive values of tumour response from a patient included in the ARTFIBio project [
18F-FDG PET images the glucose consumption of each region. Tumour cells use glycolysis rather than lipolysis as the metabolic process to produce ATP and they use more glucose than normal cells. Glycolysis is a rather inefficient process and therefore large amounts of glucose are needed for cell survival and tumour growth. The PET enhancement (standard uptake value or SUV) in tumours is due to three different mechanisms: (i) cancer cells produce more ATP outside the mitochondria, even in well-oxygenated conditions (Warburg effect [
DW-MRI measures the diffusion of protons in a medium. Its principle is based on the attenuation of the signal according to Stejskal and Tanner’s model [
DCE-MRI has been proposed by several authors for treatment monitoring [
In order to characterize the tumour and to implement new predictive models based on functional imaging data, we must ensure we can extract as much information as possible from the available data. Some of the main parameters to characterize tumour behaviour, along with radiotherapy treatment, must be initial tumour density, hypoxia, malignancy/proliferation, dose to each voxel, and timing of the dose. In this work, attention is focused on showing the relationship between ADC maps, DCE-MRI parameters, dose, and 18-F-FDG PET/CT SUV (standard uptake value). Many other types of images can show the main parameters we are interested in modelling (18F-fluorothymidine for proliferation [
This study is conducted in accordance with the Declaration of Helsinki [
The aim of ARTFIBio project ( pretreatment: MRI study (DCE-MRI + ADC) and PET/CT study (18F-FDG), first control (10–30 Gy): MRI study (DCE-MRI + ADC), second control (30 Gy–60 Gy): MRI study (DCE-MRI + ADC), three months after the treatment: PET/CT and MRI study (DCE-MRI + ADC).
For all imaging studies the patient is positioned using the RT immobilisation devices. The geometrical distortion on MRI images and registration process (rigid registration and deformable registration) were checked with an MRI phantom. Regardless, only central slices showing low distortion were analysed. For each patient and each set of images the ADC values, contrast exchange coefficients (
Scheme of the image acquisition process along the radiotherapy course.
In this paper a case study is highlighted from one patient who has three clearly differentiated volumes in a single slice: a heterogeneously vascularized tumour and a hypoxic region surrounding a necrotic area. This case is very useful to visualize and investigate the different behaviours of the tumour volumes in glucose metabolism and in treatment response.
All MRI examinations were performed on a 1.5-T scanner (Achieva; Philips Healthcare) with the patients in supine position. Routine T2-weighted, T1, DW-MRI, and DCE-MRI were obtained using the parameters showed in Table
Main parameters of MRI acquisition protocols.
Technique | TR/TE (ms) | Field of view (cm2) | Matrix size | Slice thickness (mm) | Gap | Sense factor | Contrast agent |
---|---|---|---|---|---|---|---|
T1-Turbo Spin Echo | 425/4.8 | 23 × 23 | 272 × 272 | 6 | 1 | 1.6 | — |
T2-Turbo Spin Echo | 6171/90 | 23 × 23 | 320 × 312 | 6 | 1 | 1.6 | — |
ADC |
5270/77 | 25 × 25 | 120 × 97 | 6 | 1 | — | |
ADC |
5926/85 | 25 × 25 | 120 × 97 | 6 | 1 | — | |
DCE-MRI-Dynamic T1 High Resolution Isotropic Volume Excitation (THRIVE)—7 series every 33 s | 4.1/1.97 | 24 × 24 | 120 × 120 | 6 | 1.5 | Gd |
A nonlinear model [
Main parameters of the Tofts model.
Quantity | Definition |
---|---|
|
Arterial concentration as a function of time |
|
Tissue concentration as a function of time |
|
Hematocrit volume |
|
Transfer constant from the blood plasma into the EES |
|
Transfer constant from the EES back to the blood plasma |
|
Onset time of arterial contrast uptake |
|
Whole blood volume per unit of tissue |
|
Total EES volume ( |
The relationship between all these parameters can be obtained by
Variable flip angle (VFA) spoiled gradient recalled echo scans at three flip angles variations (5°, 10°, and 15°) were utilized to calculate the voxel by voxel T10 of the GTV (gross tumour volume) of 3 different patients. The average T10 of these patients (800 ms) was applied when calculating the concentration of the analyzed patient which unfortunately did not have VFA scans themselves.
The arterial input function (AIF) was chosen in the carotid artery near the base of skull.
Whole-body PET/CT scan was carried out from head to thigh, 60 min after intravenous administration of approximately 370 MBq (±10%) of 18F-FDG on a PET/CT scanner (Discovery, GE Healthcare Bio-Sciences Corp.) with a 70 cm axial FOV, a 218 × 218 matrix. Study was acquired in 3D mode. The pixel spacing was 5.47 mm with a slice thickness of 3.27 mm. The spatial resolution to 1 cm varies from 3.99 mm to 4.56 mm. PET images were corrected for attenuation, scatter, decay, dead time, random coincidences, and slice sensitivity.
To calculate the SUV [
To reduce image noise a 3 × 3 nearest-neighbour smoothing filter was applied to the DCE-MRI, PET-CT, and ADC images. Deformable registration of the images, with the CT of treatment as reference, was performed using tailored in-house software specifically developed for the ARTFIBio project [
The relationship between the different image datasets and functional parameters was investigated in order to achieve the best possible picture of the internal tumour dynamics. Using one representative patient a plot of SUV versus ADC for the CTV is displayed in Figure
The relationship between SUV and ADC. In the hypoxic area (excluding necrotic area), high SUV values are obtained independently of the ADC value; this is explained by the addition of the Warburg effect and the Pasteur effect. In the heterogeneously vascularized area, SUV values decrease with ADC. This is likely a result of the fact that a reduction in ADC implies an increase in tumour cell density.
Several parameters can be obtained from DCE-MRI, but only the relationships between
In order to perform kinetic modeling of the tumour robust arterial input function (AIF) needs to be selected.
The AIF was chosen in the carotid artery near the base of skull for increased reproducibility since a larger variability was observed in the values of T10 in the carotid at the level of the neck (Figure
In this axial slice (bottom),
For values of
Of all the analyzed parameters,
In this figure SUV versus
On the other hand, no clear relationship has been found between ADC map and
We have generated the ADC values during the treatment for a heterogeneously vascularized tumour volume. In this case, the delivered dose to achieve an ADC value corresponding to normal tissue is much lower than for badly vascularized voxels. The influence of vascularization/oxygenation in the ADC response can be observed with the DCE-MRI studies, as shown in Figure
ADC values for a heterogeneously vascularized tumour volume are represented versus delivered dose (fractions 13th and 17th), and the colour represents the
The results presented have some similarities to those obtained by Atuegwu et al. [
The polarographic electrode has been considered by some authors as the gold standard for measuring tumour hypoxia in vivo [
18F-FDG shows different aspects of the tumour behaviour, mainly associated with tumour cell density, malignancy, and oxygenation, and the quotient between ADC and SUV has been proposed as a measurement of malignancy in breast tumours [
Using biomechanical models [
ADC maps appear to be a good option for evaluating tumour response; however their disadvantage is image distortion. Unfortunately, this cannot be corrected using standard deformable registration algorithm, but reversed gradients method looks like a very promising algorithm to solve this problem [
Multimodality imaging offers much more information about tumour behaviour than the individual datasets on their own. The relationship between different types of images must be studied in detail in order to establish a minimum set of data required to personalize the radiotherapy treatment and to optimize the treatment for each patient. This could comprise not only a gradient of dose along the treatment, but also different fractionation for each voxel.
Multicentre studies can be useful for recruitment of a large number of patients and increase the statistical power of the results, if imaging standards and protocol compliance are followed [
Voxel by voxel analysis seems possible if we consider small volumes and undistorted regions from ADC maps or corrected data.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors offer their sincerest thanks to patients who volunteered to participate in this study. The authors also would like to acknowledge Neil Burnet and Michael Simmons for their helpful comments about this text and the editorial committee for inviting them to collaborate in this special issue. The authors must be grateful for the exchange program of the Spanish Society of Medical Physics (SEFM) that funded the first author’s visit to Princess Margaret Cancer Centre. The authors thank the National Health Institute of Spain for supporting this work with ISCIII Grant PI11/02035 and the Galician Government through Project CN 2012/260 “Consolidation Research Units: AtlantTIC.”